Changes in Urinary Epidermal Growth Factor and CKD Progression: The ASSESS-AKI Study
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) and chronic kidney disease (CKD) are interconnected syndromes with AKI recognized as a clear risk factor for CKD incidence or progression. However, biomarkers of repair and epithelial cell integrity of the distal tubule, such as urinary epidermal growth factor (uEGF), may help better inform this risk, given the limitations of serum creatinine (sCr) in the setting of AKI. Methods: We enrolled 1,538 hospitalized patients prospectively in the multi-center Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) Study. We measured uEGF from samples collected during hospitalization and at 3 months post-discharge. The primary outcome was a composite of major adverse kidney events (MAKE) consisting of CKD incidence, progression, or development of end-stage kidney disease. Results: 299 (20%) patients developed the primary outcome at a median of 4.3 years follow-up. In fully adjusted models, each 1-standard deviation increase in uEGF from hospitalization to 3 months was associated with a significantly decreased risk of the composite outcome (aHR 0.71; 95% CI: 0.54-0.94; Table 1). Patients in tertile 3 (increase in uEGF) had a significantly lower risk of MAKE (aHR 0.53; 95% CI: 0.36-0.78) compared to those in tertile 2, which included patients who had no improvement in uEGF. Similar results were seen in stratified analysis by AKI status at the time of hospitalization, suggesting subclinical disease in patients without AKI.Table 1.: Association of the difference in uEGF from hospltalization to follow-up with MAKEConclusions: Urinary EGF is a marker of healthy repair after kidney injury, and increases in uEGF from hospitalization to discharge are associated with a decreased risk of MAKE in patients both with and without AKI. Funding: NIDDK Support
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".